Multimedia Tools and Applications

, Volume 64, Issue 2, pp 491–504 | Cite as

A practical use of learning system using user preference in ubiquitous computing environment

  • Hwa-Young Jeong
  • Bong-Hwa Hong


U-learning, or ubiquitous learning, has become the state-of-the-art educational trend in information technology. In ubiquitous learning environment, students now have the ability to learn anytime and anywhere. In many computer-assisted learning systems (such as e-learning, m-learning and u-learning), students must follow the lesson plans and learning environments established by the teacher. To overcome this limitation and increase effective learning, new techniques that reflect alternative learning styles, such as self-directed learning, adaptive learning and personalized learning, have been developed. These techniques have been researched extensively, but they still do not consistently reflect the specific needs of all students. In this article, a u-learning system is proposed that considers the learning level and preferences of each learner. The system provides information about the student’s preferred learning section and difficulty level of learning contents and indicates the areas that may require additional study (based on the educational history of the student), thus allowing students to set up an optimized learning environment. A topic preference vector was applied to calculate the student’s preferences, and the learning section and the difficulty level were used as each vector value in the system. To verify the effectiveness of the proposed system, an English-learning system was implemented using content from the reading comprehension section of the Test of English for International Communication (TOEIC). An experiment was conducted using a control group and a test group. The results demonstrated that the system proposed in this paper is useful for improving learning efficiency.


Ubiquitous Computing Environment Learning system User preference U-learning system 


  1. 1.
    Bin X, Anup K, David Z, Ranga R, Bing H (2010) On secure communication in integrated heterogeneous wireless networks. IJITCC 1(1):4–23CrossRefGoogle Scholar
  2. 2.
    Chen CM (2008) Intelligent web-based learning system with personalized learning path guidance. Comput Educ 51:787–814CrossRefGoogle Scholar
  3. 3.
    Chen CM (2009) Personalized E-learning system with self-regulated learning assisted mechanisms for promoting learning performance. Expert Syst Appl 36:8816–8829CrossRefGoogle Scholar
  4. 4.
    Chen CM, Chen MC (2009) Mobile formative assessment tool based on data mining techniques for supporting web-based learning. Comput Educ 52:256–273CrossRefGoogle Scholar
  5. 5.
    Chen CM, Chung CJ (2008) Personalized mobile English vocabulary learning system based on item response theory and learning memory cycle. Comput Educ 51:624–645CrossRefGoogle Scholar
  6. 6.
    Chen CM, Lee HM, Chen YH (2005) Personalized e-learning system using Item Response Theory. Comput Educ 44:237–255CrossRefGoogle Scholar
  7. 7.
    Christian W, Claudia S, Mary AG (2008) Ubiquitous laptop usage in higher education: Effects on student achievement, student satisfaction, and constructivist measures in honors and traditional classrooms. Comput Educ 51:1766–1783CrossRefGoogle Scholar
  8. 8.
    Chu HC, Hwang GJ, Tsai CC (2010) A knowledge engineering approach to developing mindtools for context-aware ubiquitous learning. Comput Educ 54:289–297CrossRefGoogle Scholar
  9. 9.
    Funda D, Aynur G (2009) Relations between online learning and learning styles. Procedia Soc. Behav. Sci 1:862–871CrossRefGoogle Scholar
  10. 10.
    Huang C, Cheng RH, Chen SR, Li CI (2010) Enhancing network availability by tolerance control in multi-sink wireless sensor networks. J of Conv 1(1):15–22Google Scholar
  11. 11.
    Jing Y, Kim J.H, Jeong D.W (2006) A Universal Model for Policy-Based Access Control-enabled Ubiquitous Computing, International Journal of Information Processing Systems, Vol.2, No.1, March.Google Scholar
  12. 12.
    Jones V, Jo JH (2004) Ubiquitous learning environment: An adaptive teaching system using ubiquitous technology, Proceedings of the 21st ASCILITE ConferenceGoogle Scholar
  13. 13.
    Kumar D, Aseri TC, Patel RB (2011) Multi-hop communication routing (MCR) protocol for heterogeneous wireless sensor networks. Int J Inf Tech, Comm and Conv 1(2):130–145Google Scholar
  14. 14.
    Lee MJW, Chan A (2005) Exploring the potential of podcasting to deliver mobile ubiquitous learning in higher education. J Comput High Educ 18(1):94–115CrossRefGoogle Scholar
  15. 15.
    Lim HM, Jang KW, Kim BG (2010) A study on design and implementation of the ubiquitous computing environment-based dynamic smart on/off-line learner tracking system. Journal of Information Processing Systems, Vol.6, No.4, DecemberGoogle Scholar
  16. 16.
    Markus B, Tyge K, Jan M, Pawlowski PV (2007) Standards for Ambient Learning Environments, 2nd conference of GI-Fachgruppe MMS(Mobilitat und mobile Information system)Google Scholar
  17. 17.
    Paolo B, Roberto P, Massimo S, Sebastiano V (2007) Traps and pitfalls of topic-biased pagerank, algorithms and models for the web-graph: fourth international workshop 2006. Banff, Canada, Nov.30-Dec. 1, Revised Papers, LNCS, Springer-Verlag, BerlinGoogle Scholar
  18. 18.
    Papanikolaou KA, Grigoriadou M (2002) Towards new forms of knowledge communication: The adaptive dimension of a web based learning environment. Comput Educ 39:333–360CrossRefGoogle Scholar
  19. 19.
    Surasee P (2010) Performance evaluation of convergence ad hoc networks. J of Conv 1(1):101–106Google Scholar
  20. 20.
    Tang C, Lau RWH, Li Q, Yin H, Li T, Kilis D (2000) Personalized courseware construction based on web data mining In Proceedings of the first IEEE international conference on web information systems engineering, 2, pp 204–211Google Scholar
  21. 21.
    Wang SJ, Tsai YR, Shen CC, Chen PY (2010) Hierarchical key derivation scheme for group-oriented communication systems. IJITCC 1(1):66–76CrossRefGoogle Scholar
  22. 22.
    Wen JR, Dou Z, Song R (2009) Personalized web search, in encyclopedia of database systems. Springer, New YorkGoogle Scholar
  23. 23.
    Yutaro O, Makoto A (2006) A proposed data sharing system for environmental learning by using PDAs. Proceedings of the ICNICONSMCL'06, IEEE Computer SocietyGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Humanitas CollegeKyunghee UniversitySeoulSouth Korea
  2. 2.Department of Information CommunicationKyunghee Cyber UniversitySeoulSouth Korea

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